Tolling System Using Both Fully and Limited Equipped Detection Systems

ABSTRACT

A method performed by a computing system includes receiving from a first detection system, a plurality of identity tokens, the plurality of identity tokens including different types of identity tokens, the first detection system having a plurality of different types of detection instruments. The method further includes using information from the first detection system, creating correlation information that correlates different types of identity tokens. The method further includes receiving from a second detection system, a number of identity tokens, the second detection system having a plurality of different types of detection instruments that is fewer than the first detection system. The method further includes using the number of identity tokens received from the first and second detection systems, identifying a vehicle passing through the second detection system.

PRIORITY INFORMATION

This application claims the benefit of U.S. Provisional Patent No.62/975,537 filed Feb. 12, 2020 and entitled “Tolling System Using BothFully and Limited Equipped Lanes,” the disclosure of which is herebyincorporated by reference in the entirety.

BACKGROUND

Conventional methods for collecting tolls on tollways typically involveissuing Radio Frequency Identification (RFID) Tags (a.k.a. toll tags) tousers. The users then place these toll tags in their vehicles. When thevehicles pass through a toll tunnel, a toll tag reader within the tunneldetects the toll tag. The account associated with that toll tag can thenbe billed accordingly.

While this approach works for highway systems, it is not generallyconducive to dense urban areas where constructing toll tunnels is notfeasible. Moreover, toll tunnels and the equipment associated therewithmay be expensive and not always reliable. Thus, there is a desire toimprove the way tolls are collected by increasing reliability withoutrelying extensively on high-priced equipment.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is best understood from the following detaileddescription when read with the accompanying figures.

FIG. 1 is a diagram showing an illustrative environment in whichdetection systems for tolling may be used, according to one example ofprinciples described herein.

FIGS. 2A, 2B, 2C, and 2D are diagrams showing an illustrative detectionsystem for detecting moving objects within a detection volume, accordingto principles described herein.

FIGS. 3A and 3B are diagrams showing an illustrative graph for use withcorrelating vehicle identity tokens, according to one example ofprinciples described herein.

FIGS. 4A and 4B are diagrams showing graphs for use with correlatingvehicle identity tokens, according to one example of principlesdescribed herein.

FIG. 5 is a flowchart showing an illustrative process for correlatingidentity tokens, according to one example of principles describedherein.

FIG. 6 is a diagram showing an illustrative computing system that may beused for correlating identity tokens, according to one example ofprinciples described herein.

DESCRIPTION

The following disclosure provides many different embodiments, orexamples, for implementing different features of the invention. Specificexamples of components and arrangements are described below to simplifythe present disclosure. These are, of course, merely examples and arenot intended to be limiting. In addition, the present disclosure mayrepeat reference numerals and/or letters in the various examples. Thisrepetition is for the purpose of simplicity and clarity and does not initself dictate a relationship between the various embodiments and/orconfigurations discussed.

Currently in the tolling industry, vehicle detection occurs at a definedpoint on the roadway. And, the detection of a vehicle identity has to beprecisely timed in order to associate the right identity (e.g., licenseplate number) with the right vehicle. This is done by synchronizingdetection events by means of triggers. For example, the vehicle isdetected at a precise point in a lane using a magnetic loop under thepavement or other expensive technologies (radar, laser, LIDAR, etc.).The positioning of detection systems (RFID transponder, cameras, etc.)must be done in a precise way (typically overhead) to be able to excludeother vehicles from being associated with that transit. This approachrequires installations that are expensive and limited to specificrequirements and tolerances for infrastructure design. This approach isvery difficult or impossible in urban areas where traffic is not alignedin lanes and where each detection point can impose strong physicaldesign restrictions.

To address this and other issues, the principles described herein aredirected to methods and systems for using a combination of both fullyequipped lanes and lesser equipped or limited equipped lanes. A fullyequipped lane may include a variety of detection instruments such ascameras, RFID systems, Bluetooth systems, etc. The detection instrumentsof the fully equipped lane may be set up so that they identify a singlevehicle passing through a specific point at a given time. A limitedequipped lane may have fewer detection systems or be set up in a waysuch that the detection systems cover a wider physical space such thatdetection is not associated with a single event at a specific location.

As a vehicle passes through a fully equipped lane, the sensors of thatfully equipped lane are able to pick up multiple identity tokens (e.g.,RFID tag, license plate, Bluetooth MAC address, etc.). As that vehiclepasses through a lesser equipped lane, it may pick up only a BluetoothMAC address or only a front license plate. However, that vehicle may bestill be identified due to correlation information that is built as thevehicle moves through multiple detection points.

The correlation information associates different identity tokens with aparticular vehicle. For example, correlation information associates anRFID tag with a license plate. This correlation may be built up as avehicle passes through fully equipped lanes. This correlationinformation may then be used to identify a vehicle passing through alimited equipped lane. For example, a vehicle's Bluetooth MAC addressmay be detected as it passes through a limited equipped lane. Becausethe correlation information allows that Bluetooth MAC address to beassociated with other identifiers such as license plate and RFID tag, anaccount associated with that vehicle may thus be charged.

Building the correlation information may involve correlating identitytokens detected by a plurality of vehicles. A vehicle may have variousidentity tokens that uniquely identify that vehicle. Such identitytokens may include, but are not limited to, a license plate number, anRFID tag, a cellular network identifier, or a network address (e.g., aMedia Access Control (MAC) address for a Bluetooth transponder). Adetection system with different detection instruments may be used tocapture the identity tokens of vehicles passing through a detectionvolume (i.e., a defined region through which vehicles pass). Forexample, an image capture device may capture license plate numbers ofvehicles passing through the detection volume. An RFID transponder maydetect the RFID tags in such vehicles. A Bluetooth transponder maydetect the MAC addresses of the Bluetooth systems in such vehicles. Thevarious detection instruments of a detection system may report theidentity tokens they detect to a processing system. That processingsystem, however, may not automatically know which identity token iscorrelated with an identity token of a different type. In other words,the system does not automatically know which license plate number goeswith which RFID tag or which Bluetooth MAC address. This is becausethere may be large numbers of vehicles passing through the detectionvolume at a particular time.

According to principles described herein, methods and systems for usingthe data collected may be employed to correlate identity tokens ofdifferent types with each other. In one example, a processing systemkeeps track of the number of occurrences an identity token is detectedas well as the number of co-occurrences of other identity tokens withthat identity token. If certain predefined criteria are met (i.e., theratio of occurrences to co-occurrences exceeding a threshold), then twoidentity tokens can be deemed as belonging to the same vehicle. Forexample, if it is determined that when a particular license plate numberis detected, it is almost always accompanied by a particular BluetoothMAC address, then it can be determined that that MAC address belongs tothe same vehicle as that license plate number.

Correlating identity tokens in this manner allows for specificimprovements to tolling technology and infrastructure. Specifically,detection points may be set up in dense urban areas rather than at agantry on a highway. An urban setting has smaller lanes, fewer lanes,and slower speeds than a highway. Urban settings also have multipleintersections with traffic control mechanisms like stop signs and stoplights.

Additionally, some detection points may be set up with fewer types ofdetection systems. For example, there may be some detection points whereonly a Bluetooth transponder is established. Because of the correlationprocess described above, the tolling systems may know to associate aparticular Bluetooth MAC address with a particular license plate, whichmay be linked to a customer account. Thus, even though only the MACaddress is detected, a customer can still be billed. Furthermore, evenif more types of detection systems are set up at a particular detectionpoints the requirements for such detection systems may be relaxed.Specifically, more cost-effective pieces of equipment may be usedbecause even if such pieces of equipment do not accurately identity aparticular identity token every time, the detection of other identitytokens which have previously been correlated to a vehicle may compensatefor the times in which a particular detection system does not accuratelydetect a particular identity token. Furthermore, tolling systems may beinstalled without the use of expensive equipment to trigger andsynchronize the passing of vehicles with the instance of detection. And,strict lane keeping requirements may be relaxed.

FIG. 1 is a diagram showing an illustrative environment in whichdetection systems for tolling may be used. According to the presentexample, an environment includes a plurality of detection systems 104 a,104 b, 104 c. Each of the detection systems 104 a, 104 b, 104 c may bein communication with a computing system 106. Each of the detectionsystems 104 a, 104 b, 104 c may be located at a different detectionpoint (e.g., geographical location).

Each of the detection points 102 a, 102 b, 102 c may be associated witha specific geographical region 100 in which tolling is implemented. Forexample, the geographic region 100 may be a city or a part of a city.The geographic region 100 may span multiple cities, states, or othergeographical definitions.

Each of the detections systems 104 a, 104 b, 104 c may correspond to adifferent detection point 102 a, 102 b, 102 c. Specifically, detectionsystem 104 a is placed at geographic location 102 a, detection system104 b is placed at detection point 102 b, detection system 104 c isplaced at detection point 102 c. Each of the detection systems 104 a,104 b, 104 c may include a set of detection instruments for detectingdifferent types of identity tokens. And, each of the detection systems104 a, 104 b, 104 c is designed to cover a detection volume. More detailon the detection instruments and detection volumes will be discussedbelow in the text accompanying FIGS. 2A and 2B.

The computing system 106 may be one or more computing devices such asservers that include both processing and storage capabilities. Thecomputing system 106 receives data from each of the detection systems104 a, 104 b, 104 c and records the number of times particular identitytokens are detected by the detection systems.

FIGS. 2A and 2B are diagrams showing an illustrative detection system104 for detecting moving objects within a detection volume 206. Todetect the moving objects, the detection system 104 includes a pluralityof detection instruments 204 a, 204 b, 204 c. In the present example,the moving objects are vehicles 202 a, 202 b, 202 c, 202 d.

Each of the detection instruments 204 a, 204 b, 204 c may be configuredto detect a specific type of identity token. For example, detectioninstrument 204 a may be an image sensor that captures video or stillimages of vehicles passing through the detection volume 206. Thedetection instrument 204 a may also include the functionality to analyzethe data and identify license plates on the vehicles passing through.For example, the detection instrument 206 may apply an Optical CharacterRecognition (OCR) function to the video or still images and identifylicense plate numbers as well as the issuing entity (e.g., state).

In a further example, detection instrument 204 b may be an RFIDtransponder. An RFID transponder emits a signal that is received by theRFID tags in the passing vehicles. In response to detecting the RFIDsignal from the transponder, the RFID tags in the vehicle will transmita unique identifier back to the RFID transponder. That unique identifiermay be used as an identity token to uniquely identify a particularvehicle.

In a further example, detection instrument 204 c may be a networktransponder that is configured to detect a network address of a vehicle.Vehicles may include network systems such as Bluetooth connectors toallow passengers to connect devices such as phones to the vehicle. Suchvehicles announce a network address such as a MAC address. The detectioninstrument 204 c may be able to detect this network address. In someexamples, the detection instrument also detects the network address ofdevices such as cell-phones or tablets within a vehicle. The networktransponder may also be for other types of networks, including cellularnetworks.

The detection system 104 may be configured to detect vehicles withinspecified time frames, which will be referred to as detection windows ordetection time windows. For example, in a particular detection window,the detection system 104 may detect various identity tokens from fourdifferent vehicles 202 a, 202 b, 202 c, 202 d. The detection time windowmay be a predefined time range and may have a default size of 1 to 40seconds, for example. Other detection time window sizes arecontemplated. In some cases, the time window may be based on the size ofthe detection volume. If, for example, the detection volume covers alarge section of a street and/or there are larger distances between thedetection instruments 204 a, 204 b, 204 c, then a larger detectionwindow may be used. Conversely, if the detection volume 206 covers asmaller section of a street and/or there are smaller distances betweenthe detection instruments 204 a, 204 b, 204 c, then a smaller detectionwindow may be used.

In some examples, the detection window may be dynamically adjusted basedon external factors such as traffic. For example, when traffic is movingslower (as detected by RADAR or LIDAR sensors associated with thedetection system 104), a larger detection window may be appropriatebecause it may take the vehicles a longer amount of time to pass throughthe detection volume. This way, the greater time window allows for anobject passing through to be detected by each of the detectioninstruments distributed throughout the detection volume. Conversely,when traffic is moving faster, a smaller detection window may beappropriate because the vehicles may take less time to pass through thedetection volume. Thus, the detection window may be adjusted inreal-time based on detected changes in traffic patterns.

A detection window may have a default value of, for example, 20 seconds.However, as mentioned above, it may be dynamically adjusted and thus mayrange from 1 seconds to 40 seconds. The detection window may be based onthe physical structure of its associated detection point. For example,the detection window may be an estimation of the time that a movingobject can trigger each of the detection instruments of a particulardetection system. The detection window may also depend on the types ofdetection instruments that are collected by a particular detectionsystem.

In some examples, the detection windows may be non-overlapping. Forexample, every 30 seconds a current detection time window stops, and anew detection time window begins. In some examples, however, detectiontime windows may be at least partially overlapping. In some examples,time windows may be defined with respect to the time at which aparticular identity token is detected. For example, a detection timewindow for a particular identity token may be defined to be plus orminus 15 seconds from when that particular identity token was detected.Any other identity tokens detected within that plus or minus 15 secondtime window will be deemed a co-occurrence.

As the vehicles 202 a, 202 b, 202 c, 202 d pass through the detectionvolume 206 for a given detection window, the system records theoccurrences for each of the identity tokens associated with each of thevehicles 202 a, 202 b, 202 c, 202 d. For example, the detection systemsmay detect 4 license plate numbers, 3 RFID tag identifiers, and 2Bluetooth MAC addresses. It may be the case that some vehicles eitherdon't have Bluetooth MAC addresses or for whatever reason the detectionsystem did not properly detect the Bluetooth MAC address for aparticular vehicle. It may also be the case that not all vehicles havean RFID tag. While the system detects a total of 4 license platenumbers, 3 RFID tag identifiers, and 2 Bluetooth MAC addresses, thisdata alone is not sufficient to determine which MAC address iscorrelated with which license plate number or which RFID tag identifier.However, by collecting more data and applying the correlation functionsdescribed herein, such correlations can be made.

FIG. 2B illustrates the detection volume 206 at a different detectionwindow. The different detection window may be, for example, on adifferent day around a similar time. In this new detection window, thedetection instruments 204 a, 204 b, 204 c may detect identity tokensfrom vehicles 202 a, 202 e, and 202 f. Thus, if the same pair of licenseplate number and MAC address are detected as the detection window ofFIG. 2A, then it may be more likely that those two are correlated asthere has been more than one co-occurrence of that pair. However, thesystem may have higher thresholds of detecting the co-occurrence of twoidentity tokens before officially deeming two identity tokens as beingcorrelated. Thus, the system will continue to collect data. As vehicle202 a passes through more detection points at different geographiclocations and at different time windows, the computing system 106 maydetermine with sufficient probability that two different identity tokensare correlated with the same vehicle.

The computing system may thus record the occurrences and co-occurrencesof identity tokens for vehicles as they pass through various detectionvolumes within various detection windows. The collected data may bestored in a variety of formats or data structures. One such datastructure is a graph.

FIG. 2C illustrates a detection system 104 b at a different locationwithin the geographic region 100. In the present example, detectionsystem 104 b differs from detection system 104 a in that it has fewerdetection instruments. In the present example, detection system 104 bincludes detection system 204 a and detection system 204 c. Continuingthe examples from above, detection instrument 204 a may be an imagecapture device that is capable of taking images of license plate numbersand reading the license plate from those images. Detection instrument204 c may be, for example, a Bluetooth reader that is able to detect thenetwork address of a Bluetooth system in passing vehicles.

The detection system 104 b may be placed in parts of the geographicregion where placement of additional instruments (such as an RFIDtransponder) are impractical. An RFID transponder often requires a largegantry with expensive equipment. While such a setup may be practical forlarge open highways, it may not be practical in smaller urban streets.However, using the techniques described below, the detection system 104b may still be able to accurately identify passing vehicles at suchlocations even with fewer detection instruments.

FIG. 1D illustrates a similar situation in which detection system 104 cincludes fewer detection instruments. In the present example, thedetection system 104 c includes detection instrument 204 a and detectioninstrument 204 b. In some examples, the reason for having fewerdetection instruments in a particular detection system may be cost.Detection equipment may be expensive to set up, operate, and maintain.It is desirable to reduce such costs and still have an effective way ofidentifying passing vehicles at each detection system. Using thetechniques described herein, some of the detection systems within ageographic region may utilize fewer detection instruments, and stillmaintain accurate identification using the pairing techniques describedherein.

In some examples, the detection systems that have more detectioninstruments may be placed on a gantry over a highway. Detection systemsthat have fewer detection instruments may be placed in an urban settingwith slower speeds and traffic control mechanisms such as stop lightsand stop signs. The detection systems with fewer detection instrumentsmay have a larger detection time window and a larger detection volume inorder to better capture the moving objects moving through at slowerspeeds.

FIGS. 3A and 3B are diagrams showing an illustrative graph for use withcorrelating vehicle identity tokens. A graph is a data structure thatincludes a plurality of nodes (represented by circles) and edges(represented as lines connecting the nodes). Whenever the occurrence ofan identity token is detected, a corresponding node within the graph maybe incremented. Whenever the co-occurrence between two identity tokensare detected within a detection window, the edge between the twocorresponding nodes may be incremented.

FIG. 3A illustrates an example of a graph after a set of identity tokenshas been detected within a detection window. Specifically, the graphincludes four nodes 302 a, 302 b, 302 c, 302 d. Each node is associatedwith a different identity token. For the sake of example, nodes 302 aand 302 d are associated with a license plate number, node 302 b isassociated with an RFID tag identifier, and node 302 c is associatedwith a network address.

The value for each of the nodes represents the number of occurrences fortheir respective identity tokens. An occurrence occurs any time anidentity token is detected by a detection system within a givendetection window. In the present example, the value for each of thenodes is 1.

The value for each of the edges 304 between the nodes indicates thenumber of co-occurrences for those nodes. For example, when the licenseplate number associated with node 302 a is detected within the samedetection window and detection volume as the RFID tag identifierassociated with node 302 b, then the value of edge 304 ab is incrementedby 1. Similarly, edges 304 ac, 304 ad, 304 bc, 304 bd, 304 cd are alsoincremented by 1.

FIG. 3B illustrates the stated of the graph after some additionalidentity tokens are detected within a different detection window or at adifferent detection volume. Specifically, four new identity tokens aredetected. Two of the four identity tokens correspond to nodes 302 c and302 d. Thus, the value in each of those nodes is incremented by one, asthe detection of such identity tokens represents an occurrence.Additionally, the edge 304 cd between nodes 302 c and 302 d isincremented, as the presence of those two identity tokens within thesame detection window represents a co-occurrence.

The other two identity tokens detected within the new detection windoware new and thus new nodes 302 e and 302 f are created. Furthermore, thevalue in the new nodes 302 e, 302 f are incremented to 1, as thedetection of the identity tokens represents an occurrence. And, theedges 304 ce, 304 cf, 304 de, 304 df, 304 ef are incremented, as thedetection of the associated identity tokens within the same detectionwindow represent a co-occurrence.

Furthermore, certain edges are decremented. Specifically, the edgesbetween two nodes are decremented if one of the identity tokensassociated with one of the two nodes is detected within a time windowwithout detection of the identity token associated with the other of thetwo nodes. Thus, in the present example, because nodes 302 c and 302 dwere detected without nodes 302 a and 302 b, edges 304 ac, 304 bc, 304ad, and 304 bd are decremented. When one identity token is detectedwithout another identity token within the same time window, it is lesslikely that those two nodes are associated with the same vehicle. As thesystem collects more data from additional time windows at variousdetection volumes, the graph will continue to expand and form new nodesand new edges and will start to look more like the graph 400 of FIG. 4A.

FIGS. 4A and 4B are diagrams showing graphs for use with correlatingvehicle identity tokens. As the detection systems collect more data anddetect more identity tokens in more detection windows, the graph 400continues to build as new connections are made and broken. Specifically,several nodes 402 and edges 404 are created. After a period of time,however, sections of the graph will break off, as shown in FIG. 4B.

FIG. 4B illustrates a stabilized portion 410 of a graph. Specifically,after more data is collected, identity tokens associated with aparticular vehicle or moving object will separate and form a stabilizedportion. Specifically, node 406 a corresponding to a license platenumber has 45 occurrences. Node 406 b corresponding to an RFID tagidentifier has 47 occurrences. Node 406 c corresponding to a networkaddress has 46 occurrences.

The number of occurrences for each of the nodes 406 a, 406 b, 406 may bedifferent for a variety of reasons. One of the detection instruments maynot accurately detect a particular identity token. For example, due topoor weather conditions, the image sensor may not get a clear picture ofa license plate. Furthermore, it may be the case that a person has movedan RFID tag to another vehicle. It may also be the case that a networksystem (such as a Bluetooth system) in a vehicle is not active when itpasses through a detection point. The number of co-occurrences, asdenoted through the values of the edges 408 ab, 408 ac, 408 bc betweenthe nodes 406 a, 406 b, 406 c, may also be different for similarreasons.

FIG. 5 is a flowchart showing an illustrative process for correlatingidentity tokens. According to the present example, the method 500includes a process 502 for receiving an identity token. The identitytoken may be, for example, a license plate number, an RFID tagidentifier, a network address, or other token that uniquely identifies avehicle. The identity token may be received by a computing system (e.g.,106) after being detected by a detection instrument of a detectionsystem as a vehicle or other moving object passes through a detectionvolume within a detection window.

The method 500 further includes a process 504 for determining whetherthat identity token corresponds to a current node within the graph. Forexample, if the received identity token is a license plate number, thenthe computing system determines whether that specific license platenumber has previously been detected and already has an associated nodein the graph. If it is determined that there is no corresponding node(504, No), then the method 500 proceeds to process 506, at which point acorresponding node is created. This new node may be created with a valueof 1. If there is already a corresponding node (504, Yes), then themethod 500 proceeds to step 508 at which the already created node isincremented.

The method 500 further includes a process 510 for incrementing edges ofco-occurrences within the detection time window. Specifically, afternodes have been created and/or incremented for each identity tokenwithin a specific time window at a specific detection volume, the edgesbetween each of those nodes are incremented. For example, if the licenseplate number was detected with a number of other identity tokens in thesame time window, then each of those other identity tokens will havecorresponding nodes in the graph. Thus, the edges between the nodeassociated with the license plate number and the nodes associated withthe other identity tokens will be incremented.

The method 500 further includes a process 512 for decrementing edges ofnon-co-occurrences within the detection window. Specifically, aparticular identity token may already have a node in the graph, and thatnode may be connected to other nodes through various edges. However,during the current time window, that particular identity token may notbe co-occurrent with some of the other identity tokens. In such case,those previous co-occurrences may have been due to two separate vehiclespassing though the detection volume at the same period of time ratherthan due to the two identity tokens being associated with the samevehicle. Thus, such edges are decremented.

In some examples the detection systems may detect other features ofmoving objects that do not necessarily uniquely identify a movingobject. Such features may be, for example, color, length, volume, model,make, etc. Such features will be referred to as qualifiers. Thequalifiers may be identified using various detection instruments. Forexample, the image sensor that is used to identify the license platenumber may also identify qualifiers. Specifically, an image analysisfunction may be applied to identify make, model, and color of a vehicle.Other detection instruments such as LIDAR or RADAR instruments may beused to identify size, length, or volume characteristics of vehicles.Identified qualifiers may be associated with a particular identitytoken, such as license plate and then be associated with an objectidentity.

Qualifiers are thus detection of characteristics that are not unique. Asimilar approach to what has been described above may be used to buildrelationships between qualifiers and identity tokens. Because qualifiersare not unique, several instances of the same qualifier may appearwithin the same detection window at the same detection point. Forexample, the system may detect several white cars within a detectionwindow.

In order to make the co-occurrences process converging to a stableassociation with a set of identity tokens that represents a movingobject, the qualifier should have some peculiar characteristics. Therange of value of the qualifier type should be wide enough to guaranteethat, statistically, in a certain finite time window of correlation, theprobability of always finding the same subset of qualifiers in all thedetection points is reasonably low. For example, if a qualifier is thenumber of axles in an urban environment, the probability of detectingmultiple two axle vehicles in a detection point during a detectionwindow is very high, preventing the process to converge to a uniqueassociation between a qualifier and an identity token. Two differentapproaches may be used to address this.

In one example, a detection instrument may be capable of extracting moreinformation. For example, a vehicle axle detection system may not onlyidentify the number of axles but also identify the distance betweenaxles, which may be a more unique characteristic. Once the number ofaxles and the distances is processed by a clustering function (forexample k-mean) with a sufficient number of centroids, the probabilitythat a vehicle with the same exact characteristic (the same centroid isassigned to) is always present in all the detection points issufficiently lower than 1. This allows the process to converge to astrong association after few detections with very high probability.

In another example, a detection instrument such as an image sensor maydetect both a license plate number (identity token) as well as thevehicle color (qualifier). In this case the graph co-occurrence is builtas a synchronized co-occurrence. That, in some way, it is similar to atraditional triggered approach. Nevertheless, the counters and specificthresholds are still built in the same way described above. Usingqualifiers in this manner can help reduce or eliminate detection errors,changes, or anomalies (e.g., a license plate moved from a white to ablack vehicle).

In some examples, a “quorum” based approach is used. For example, amoving object identity may be associated with a set of qualifiers. Inother words, each identity token in the graph component (an isolatedsubgraph of connected nodes) associated with a moving object identitywill produce a weighted set of associations with a subset of qualifiers.If a certain quorum is reached (for example the 80%) of accordance amongthe identity token, that qualifier is connected to the vehicle identity.

In some examples, each time a co-occurrence edge is updated the systemmay check the ratios between occurrences and co-occurrences (andoptionally other parameters such us frequency of the simultaneities,last date seen, etc.). In some examples, if one or both parameters fallunder predefined thresholds (numbers between 0 and 1) the co-occurrencewill be deleted. In other words, whatever value an edge has, it may bedeleted if the occurrences far outweigh the co-occurrences.

As the system evolves and brings in more data, the co-occurrences graph400 becomes more and more fragmented (loosely connected). Each group ofisolated edges will stabilize and will represent, with a very highprobability, the collection of identity tokens integral to a particularmoving object. This aggregation will be referred to as a moving objectidentity. The moving object identity is a statistical property of theobject. Nevertheless, if the system is properly configuring theaggregate the probability of an error in the aggregation decreaseexponentially with the number of detections.

The system may also distinguish between unambiguous and ambiguousidentity. Specifically, different identity token types may havepre-assigned ambiguity values. A front license plate and rear licenseplate may have a low level of ambiguity, because these things are lesslikely to be changed. An RFID tag may have a slightly higher level ofambiguity because customers may switch RFID tags between differentvehicles. A fixed Bluetooth MAC address may have a higher level ofambiguity because a Bluetooth MAC address may be associated with otheritems in a vehicle besides the vehicle itself, such as cell phones,tablets, etc.

It also possible to define an ambiguity level based on, for example, thenumber of transponders associated with an object. The number oftransponders can have a statistical distribution where 0 has a 40%probability, 1 transponder 55%, 2 transponder 4%, etc.). A vehicleidentity with 5 transponders will have a higher level of ambiguity thana vehicle with one transponder. An identity with 2 rear license platewill have a very high level of ambiguity.

FIG. 6 is a diagram showing an illustrative computing system that may beused for correlating identity tokens. For example, the computing system600 may be used to perform the functions associated with the computingsystem 106. Other functions described herein may also be performed bycomputing systems such as computing system 600. According to certainillustrative examples, the computing system 600 includes a memory 604which may include software 606 and a data store 608. The processingsystem 600 also includes a processor 510, a network interface 614, and auser interface 612.

The memory 604 may be one of several different types of memory. Sometypes of memory, such as solid-state drives, are designed for storage.These types of memory typically have large storage volume but relativelyslow performance. Other types of memory, such as those used for RandomAccess Memory (RAM), are optimized for speed and are often referred toas “working memory.” The various types of memory may store informationin the form of software 606 and data in the data store 608.

The computing system 600 also includes a processor 610 for executing thesoftware 606 and using or updating the data 508 stored in memory 604.The software 606 may include an operating system and any other softwareapplications a user may wish to install. In some examples, the computingsystem 600 may be associated with a user. In such case, the software 606may be an application to render web content, such as a browser. Thesoftware 606 may include machine readable instructions of a computerprogram product that when executed, perform the functions describedabove.

The user interface 612 may include a number of input devices such as amouse, touchpad, or touchscreen that allow the user to interact with thecomputing system 600. The user interface 612 may also include a numberof different types of output devices such as a monitor or a touchscreen.The user interface allows the user to interact with the processingsystem 600 in a manner as described above.

The network interface 614 may include hardware and software that allowsthe processing system 600 to communicate with other processing systemsover a network 616. The network interface 614 may be designed tocommunicate with the network 616 through hardwire media such asEthernet, coaxial, fiber-optic, etc. The network interface 614 may alsobe designed to communicate with the network 616 using wirelesstechnologies.

Some examples of processing systems described herein may includenon-transitory, tangible, machine readable media that include executablecode that when run by one or more processors may cause the one or moreprocessors to perform the processes of methods as described above. Somecommon forms of machine-readable media that may include the processes ofmethods are, for example, floppy disk, flexible disk, hard disk,magnetic tape, any other magnetic medium, CD-ROM, any other opticalmedium, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip orcartridge, and/or any other medium from which a processor or computer isadapted to read.

The foregoing outlines features of several embodiments so that thoseskilled in the art may better understand the aspects of the presentdisclosure. Those skilled in the art should appreciate that they mayreadily use the present disclosure as a basis for designing or modifyingother processes and structures for carrying out the same purposes and/orachieving the same advantages of the embodiments introduced herein.Those skilled in the art should also realize that such equivalentconstructions do not depart from the spirit and scope of the presentdisclosure, and that they may make various changes, substitutions, andalterations herein without departing from the spirit and scope of thepresent disclosure.

What is claimed is:
 1. A method performed by a computing system, themethod comprising: receiving from a first detection system, a pluralityof identity tokens, the plurality of identity tokens including differenttypes of identity tokens, the first detection system having a pluralityof different types of detection instruments; using information from thefirst detection system, creating correlation information that correlatesdifferent types of identity tokens; receiving from a second detectionsystem, a number of identity tokens, the second detection system havinga plurality of different types of detection instruments that is fewerthan the first detection system; and using the number of identity tokensreceived from the first and second detection systems, identifying avehicle passing through the second detection system.
 2. The method ofclaim 1, wherein the plurality of different types of detectioninstruments of the first detection systems includes at least two of: aRadio Frequency Identifier (RFID) reader, an imaging system, a Bluetoothnetwork transponder, and a cellular network transponder.
 3. The methodof claim 2, wherein the second detection system includes at least oneof: a Radio Frequency Identifier (RFID) reader, an imaging system, aBluetooth network transponder, and a cellular network transponder. 4.The method of claim 1, wherein the different types of identity tokensinclude an RFID tag, a front license plate, a rear license plate, aBluetooth Media Access Control (MAC) address, and a cellular networkaddress.
 5. The method of claim 1, wherein identifying a vehicle passingthrough the second detection system comprises detecting a singleidentity token with the second detection system and using thecorrelation information to associate the single identity token withother identity tokens obtained from the first detection system.
 6. Themethod of claim 1, wherein the first detection system is geographicallyseparated from the second detection system.
 7. The method of claim 1,wherein the first detection system is positioned on a gantry on ahighway.
 8. The method of claim 1, wherein the second detection systemis positioned in an urban setting.
 9. The method of claim 1, wherein thesecond detection system uses a larger detection time window than thefirst detection system.
 10. The method of claim 1, wherein the seconddetection system uses a larger detection volume than the first detectionsystem.
 11. A system comprising: a processor; and a memory havingmachine readable instructions that when executed by the processor causethe system to: receive from a first detection system, a plurality ofidentity tokens, the plurality of identity tokens including differenttypes of identity tokens, the first detection system having a pluralityof different types of detection instruments; use information from thefirst detection system, creating correlation information that correlatesdifferent types of identity tokens; receive from a second detectionsystem, a number of identity tokens, the second detection system havinga plurality of different types of detection instruments that is fewerthan the first detection system; and using the number of identity tokensreceived from the first and second detection systems, identifying avehicle passing through the second detection system.
 12. The method ofclaim 11, wherein the plurality of different types of detectioninstruments of the first detection system includes an image captureinstrument, a network interface system, and a Radio Frequency Identifier(RFID) reader.
 3. The system of claim 12, wherein the plurality ofdifferent types of detection instruments of the second detection systemincludes an RFID instrument and an image sensor instrument.
 14. Thesystem of claim 12, wherein the plurality of different types ofdetection instruments of the second detection system includes an RFIDinstrument and a network interface instrument.
 15. The system of claim12, wherein the plurality of different types of detection instruments ofthe second detection system includes a network interface instrument andan image sensor instrument.
 16. The system of claim 11, wherein thesecond detection system is placed at a geographic region that is morerestrictive than a geographic region where the first detection system isplaced.
 17. The system of claim 11, wherein the first detection systemis placed on a highway.
 18. The system of claim 11, wherein the seconddetection system is placed on an urban street.
 19. A system comprising:a first detection system comprising: a first detection instrument of afirst type; a second detection instrument of a second type; and a thirddetection instrument of a third type; a second detection systemcomprising, a fourth detection instrument of the first type, the seconddetection system having fewer types of detection instruments than thefirst detection system; and a control system configured to: receive fromthe first detection system, a plurality of identity tokens, theplurality of identity tokens including different types of identitytokens; with information from the first detection system, createcorrelation information that correlates different types of identitytokens; receive from the second detection system, a number of identitytokens; and use the number of identity tokens received from the firstand second detection systems, identifying a vehicle passing through thesecond detection system.
 20. The system of claim 19, wherein the seconddetection system is placed at a geographic region that is morerestrictive than a geographic region where the first detection system isplaced.